System and method for testing a biological sample

ABSTRACT

The method for testing a biological sample operates on a testing system. The testing system generally comprises an instrument that is configured to acquire data from a biological sample and a processor. In performing the method for testing, the instrument acquires data from the biological sample, and the processor compares the acquired data to predefined data criteria. Responsive to comparing the acquired data to the data criteria, the instrument may be adjusted, and another data set acquired. In one disclosed example of the testing system, a mass spectrometer acquires data from a biological sample. The acquired data is compared to predefined spectrum criteria. Responsive to the comparison, the mass spectrometer may be directed to resample the biological sample or proceed to another sample.

TECHNICAL FIELD

[0001] The field of the present invention is testing methods forbiological samples. In a particularly disclosed example, a system havinga processor is used to implement the disclosed testing method.

BACKGROUND

[0002] Instruments, such as the mass spectrometer, are now routinelyused to assist in identifying components of a biological sample. Inparticular, the MALDI time-of-flight (TOF) mass spectrometer has provenparticularly useful in making biological determinations, such asgenotyping or identifying single nucleotide polymorphisms.

[0003] The MALDI TOF mass spectrometer generally operates by directingan energy beam at a target spot on a biological sample. The energy beamdisintegrates the biological material at the target spot, with thedisintegrated component material being hurled toward a measurementmodule. The lighter component material arrives at the measurement modulebefore the heavier component material. The measurement module capturesthe component material, and generates a data set indicative of the massof the component material sensed. Typically, the data set is generatedas a two dimensional spectrum, with the x-axis representing a massnumber, and the y-axis representing a quantity number.

[0004] The data, which is often presented as a data spectrum, typicallyhas peaks positioned on a generally exponentially decaying baseline.Each peak should ideally represent the presence of a component of thebiological sample. Unfortunately, due to chemical and mechanicallimitations, the data spectrum is replete with noise, so an accuratedetermination of biological components is challenging. Indeed, it takesan experienced operator to accurately read and interpret a dataspectrum. However, the efforts of even the best trained human operatorcan suffer from inaccuracies and errors. Since the results derived fromthe data spectrum are often used in health care decisions, mistakes canbe devastating. Therefore, operators are trained to make a determinationonly when certain of the result. In such a manner, a great number oftests result in no-calls, where the operator cannot clearly identify adata result.

[0005] Accordingly, the use of mass spectrometers risks an unacceptablylarge number of inaccurate calls if the operator is applying a ratherloose standard to the data spectrum. Alternatively, the use of massspectrometers becomes highly inefficient if the operator discards alarge number of tests due to an inability to confidently make a call.

[0006] To assist the operator in making calls, the mass spectrometer mayprovide a level of data filtering. Typically, the data filteringattenuates a set magnitude of noise, thereby more conspicuously exposingvalid peaks. However, such a filtering technique may actually maskimportant valid peaks, resulting in an incorrect analysis.

[0007] Modern trends in biotechnology are taxing the capabilities ofinstruments such as mass spectrometers and their operators. For example,mass spectrometers are now being used to identify single nucleotidepolymorphisms (SNPs). However, SNPs may produce only slight peaks on thedata spectrum, which are easily missed by an operator or buried inbackground noise. Further, mass spectrometers are also now being usedfor multiplexing, where multiple gene reactions may be present in asingle sample. In such a manner, the resulting peaks may be smaller,more difficult to identify, and there may be more combinations of falsereadings. With such a complicated data spectrum, it is becoming moredifficult for an operator to confidently determine if a valid peakexists for a particular genetic component.

[0008] The mass spectrometer, therefore, provides a data spectrum thatis difficult for an operator to interpret. Even under the best ofconditions, the operator is likely to either make identifications wherethe call should not have been made, or is likely to discard goodacquired data because of perceived ambiguity. Accordingly, there existsa need for a more efficient and accurate method and system foridentifying a biological sample.

SUMMARY OF THE INVENTION

[0009] It is therefore an object of the present invention to provide atesting system and method that overcomes the deficiencies in the priorart. It is also an object of the present invention to provide forefficient and accurate biological identification.

[0010] The method for testing a biological sample in accordance with theinvention utilizes a testing system. The testing system generallycomprises a processor and an instrument that is configured to acquiredata from a biological sample. In performing the testing method, theinstrument acquires data from the biological sample, and the processorcompares the acquired data to predefined data criteria. Responsive tocomparing the acquired data to the data criteria, the instrument may beadjusted, and another data set acquired. In one disclosed example of thetesting system, a mass spectrometer acquires data from a biologicalsample. The acquired data is compared to predefined spectrum criteria.Responsive to the comparison, the mass spectrometer may be directed toresample the biological sample or proceed to another sample.

[0011] Advantageously, the disclosed method and system for testing abiological sample provides automated control of a mass spectrometer.More particularly, the new testing method enables a highly accuratedetermination of a biological sample with minimal manual intervention.Accordingly, biological samples may be identified and diagnostic testsperformed with a degree of precision, speed, and accuracy not availablefrom known testing systems.

BRIEF DESCRIPTION OF THE DRAWINGS

[0012]FIG. 1 is a block diagram of a testing system in accordance withthe present invention;

[0013]FIG. 2 is a flowchart of a testing process in accordance with thepresent invention;

[0014]FIG. 3 is a flowchart of a testing process in accordance with thepresent invention that illustrates automated control of a testinginstrument;

[0015]FIG. 4 is a flowchart of a testing process in accordance with thepresent invention that illustrates over-sampling a biological sample;

[0016]FIG. 5 is a flowchart of a testing process in accordance with thepresent invention that illustrates acquiring data from multiple samplesto establish the presence of a biological relationship; and

[0017]FIG. 6 is an illustration of a computer display showing resultsfrom a testing system in accordance with the present invention.

DETAILED DESCRIPTION

[0018] Referring now to FIG. 1, an example testing system 10 for testingabilogical sample is illustrated. Generally, the testing system 10contains a real time (RT) workstation 12 which includes a series ofcontrollers that retrieve assay design parameters from the databaseserver 13 and directs the acquisition and processing of data indicativeof the biological sample from the mass spectrometer 14. The processeddata or genotyping results are then downloaded into a directory in thedatabase server 13.

[0019] With the testing system 10 generally disclosed, individualcomponents will now be described. The testing system 10 has a RTworkstation 12 that may be, for example, a computer system havingstorage and computational components, including one or more controllers.In a preferred embodiment, the RT workstation is made up of onecontroller 30 which acquires assay design specifications from thedatabase server 13, includes another controller 31 which automaticallyaligns the laser on the chip using an image system, controls the motormovement of the assay substrate at the mass spectrometer, and acquiresthe data signal directly from the mass spectrometer, and includesanother controller 32 that communicates with the controller 31 byreceiving a data signal and providing instruction for additional dataacquisition. Additional data acquisition may be dependent on the qualityof the data previously obtained. The data is preferably stored on alocal hard drive of the RT workstation 12 until the results from all thesamples are compiled. The compiled data is stored in a directory in thedatabase server 13. The RT workstation 12 preferably has a display 16for visually communicating test results and status information. In apreferred embodiment, the RT workstation 12 is a computer, such as anIBM compatible personal computer system, communicating with the massspectrometer using a known communication standard, such as a parallel orserial interface. It will be appreciated that the workstation andcontrollers may be alternatively embodied. For example, the RTworkstation 12 may be integral to the mass spectrometer 14 or anothersystem component, or the workstation and controller 12 may be placed ata remote location from the mass spectrometer. In such a manner thenetwork topography, such as a wide area network or a local area network,would provide a communication path between the mass spectrometer 14 andthe RT workstation 12. Although the RT workstation 12 is preferably astandalone computer device, it will be appreciated that one or more ofthe controllers may be, for example, a microprocessor or otherprogrammable circuit device capable of performing a programmed process.

[0020] The mass spectrometer 14 is preferably a MALDI Time-of-Flight(TOF) instrument. Such a device is more fully described in co-pendingU.S. patent application Ser. No. 09/663,968, filed Sep. 19, 2000 andentitled, “SNP Detection Method”, and U.S. patent application Ser. No.09/285,481, filed Apr. 5, 1999 and entitled, “Automated Process Line”,both of which are incorporated herein by reference in their entirety.The mass spectrometer 14 is configured with an interface to communicatewith the workstation controller 12. The interface preferably conforms toa known data communication standard, for ease of connection. Although asingle interface may enable the controller 12 to both receive data fromthe mass spectrometer 14 and send instructions to the mass spectrometer14, two or more separate interfaces may be used. Although the preferredtest system 10 incorporates a MALDI TOF mass spectrometer, it will beappreciated that other types of analytical instruments may be used.

[0021] The testing system 10 may provide the database server 13 with oneor more databases, such as database 18, database 19, database 20,database 21 and database 22 stored in direct access storage devices. Itwill be appreciated that other forms of data storage may be used.However, a structured database provides a convenient format for storingand retrieving data. In a preferred embodiment one of the databases,such as database 18, stores assay design information, a database 19stores genotyping profiles, a database 20 stores allelotyping profiles,database 21 stores sample identification information, while the otherdatabase 22 stores test results for later analysis. It will beappreciated that fewer or more databases may be used to store assay andtest information. The database server 13 may also contain one or morecontrollers such as controller 23 and controller 24. In a preferredembodiment the controller 23 monitors the data acquisition of theindividual samples on the assay substrate or chip. Once the data isreceived from all samples in the assay, the data monitoring controller23 downloads all or part of the assay information and stores theinformation in a directory in the test results database 22. Thecontroller 24 imports the data into a directory in the results database22.

[0022] The RT workstation 12 has sufficient processing ability toextract assay design information from the assay design database 18, andto convert the assay design information into a format for providingspecific directions to the mass spectrometer 14. For example, thecontroller may access the database 18 and request a specific assaydesign. The specific assay may be set up to provide a microtiter platewith hundreds, or even thousands, of samples on each plate. The test mayrequire that samples be tested in a specific order, and based upon theresult from previous tests, the order may be adjusted, or some samplesmay even be eliminated from the assay. The RT workstation receives theassay design information and converts the assay design information intocommands for the mass spectrometer 14. Upon starting the assay, the RTworkstation 12 sends initialization commands to the mass spectrometer 14consistent with the assay design.

[0023] Extracting an assay design from a database and generating massspectrometer commands may be a time consuming and processor intensiveoperation. It would be particularly undesirable for the extractionprocess to interfere with the more real-time control of the massspectrometer. Accordingly, it is preferred that the RT workstation 12perform a database extraction process, and database storage functions,as background tasks, or at a time when such tasks will not materiallyinterfere with the more real-time control of the mass spectrometer 14.As used herein, real-time control refers to the ability of the RTworkstation 12 to receive data from the mass spectrometer 14, processthe data, and provide command direction to the mass spectrometer in anautomated and efficient manner.

[0024] The RT workstation 12 defines physical map of the biologicalsamples on the assay plate or chip by manual input of the information bythe operator or an automated scanning system such as a bar code reader.

[0025] A mass spectrometer 14 receives the biological sample foranalysis and generates an electrical data signal representative ofgenotype information associated with the sample tested under directionfrom the real time workstation 12. The instrument is initialized when itis provided with specific data acquisition parameters, either manuallyor in a default mode. The acquisition parameters may include the numberof laser shots per spot, the maximum number of raster per sample, andvoltage, delay time, calibration constants and other parameters thatwill be well-known to those skilled in the art. The mass spectrometer isinitialized according to test assay parameters, and acquires dataindicative of the biological samples. More particularly, the dataacquired by the mass spectrometer is typically in the form of anelectronic data spectrum. The electronic data spectrum can be retrievedby the RT workstation.

[0026] Biological samples are analyzed when the RT workstation 12directs the automatic alignment of the mass spectrometer laser on assaysurface or chip using an imaging system and controls movement of thelaser from sample to sample and assay surface to assay surface whenmultiple assay surfaces or chips are held in a multi-component holder.

[0027] Biological or genotyping information is acquired directly fromthe mass spectrometer 14 by the RT workstation 12. The signal isconverted into amass data spectrum by the RT workstation 12 where agenotype is determined. If the sample genotype cannot be called, the RTworkstation 12 will recognize the situation and may direct an adjustmentto the mass spectrometer 14. For example, if the acquired spectrum hasan unacceptably high signal to noise ratio, the workstation controller12 may direct the mass spectrometer 14 to test the same sample again,but may adjust the mass spectrometer 14 to direct its beam at adifferent spot on the sample, or may select alternative power settingsor measurement filters. In another example, the controller 12 may directthe mass spectrometer 14 to take a series of data sets from the samesample until the standard deviation in the aggregate results achieves adesired degree of certainty. It should be understood that, even thoughthe same sample may be tested multiple times, each test will be takenfrom a unique spot on the sample.

[0028] Referring now to FIG. 2, a method of testing a biological sampleis shown. The method of testing first predefines spectrum criteria thatpredicts the presence of a biological relationship in block 21. Thepredefined spectrum criteria will vary depending on the assay being run.For example, the spectrum criteria may be set to assure a minimumallelic ratio is exceeded. In this regard, the spectrum criteria may beset to reject acquired data where the allelic ratio is below athreshold, such as 5%. In another example, the presence of specificmarkers may be required to validate acquired data. In another example,the spectrum criteria may require that a peak exceed a signal to noisefigure before accepting the acquired data as valid. Further, statisticalmethods may be applied to the acquired data, or sets of acquired data,to determine if a particular peak is statistically signification. Usingsuch a statistical method may dramatically increase the accuracy ofcalling the composition of a biological sample. U.S. application Ser.No. 09/663,968 filed Sep. 19, 2000 teaches a specific example of astatistical method as applied to acquired spectrum data. It will beappreciated that the spectrum criteria can be defined in numerous waysconsistent with the teaching of this application.

[0029] With the spectrum criteria predefined, block 22 shows that theassay design is defined, and then preferably stored in a database foruse in controlling the instrument. In a preferred embodiment, theinstrument is a MALDI TOF mass spectrometer. It will be appreciated thatother instruments may be substituted. The defined assay design is usedto generate the initial settings for the instrument, and then is furtherused to direct the instrument during the assay test.

[0030] Biological samples are then positioned in block 23 for test inthe instrument. The samples are positioned preferably on a holder suchas a microtiter plate. It will be appreciated that other types ofholders, such as test tubes or chips, may be substituted for amicrotiter plate holder. Although it is more convenient to place allsamples for one assay on a single holder, samples for a single assay maybe placed on multiple holders.

[0031] The holder is positioned in the instrument, as indicated in block24. The holder may be manually positioned, or may be positioned underrobotic control. If the holder is robotically controlled, theninformation extracted from the assay design may be used to direct therobotic control to place the proper holder in the instrument. Ifmanually positioned, a visual display may be used to assist the humanoperator in identifying and verifying the proper holder.

[0032] Blocks 25-28 represent the real time control of the instrumentand will be described further below. This real time control enables theautomated and efficient operation of the instrument, and providesaccuracies and repeatabilities in test results that are not available inknown systems.

[0033] In block 25, the instrument acquires a data set from a biologicalsample. In a preferred embodiment, the acquired data is in the form ofan acquired data spectrum. In the exemplary system described in the '968Application, the data set is generated by first finding height of eachpeak, then extrapolating noise profile, and finding noise of each peak,next calculating signal to noise ration, and finding residual error, andcalculating and adjusting signal to noise ratio, and developing aprobability profile, and determining peak probabilities, and determiningallelic penalty, and adjusting peak probability by allelic penalty, andcalculating genotype probabilities, and testing ratio of genotypeprobabilities.

[0034] The acquired data is evaluated in block 26. In a preferredembodiment, the acquired data is compared against the spectrum criteriapreviously defined. As described above, this comparison can be, forexample, a comparison of peak strength, peak position, markers, s/nratio, allelic ratio, or a statistical calculation. Further, thecomparison may be multi-dimensional, for example, requiring first that aparticular marker be located and then testing that an appropriate signalto noise ratio exists. It will also be appreciated that the comparisonstep may use data from multiple acquired data sets, for example, tocalculate the standard deviation for the group. Accordingly, thecomparison will compare the standard deviation in the group of data setsto determine if the results should be derived from the newly acquireddata.

[0035] Responsive to the comparison, the workstation controller adjuststhe instrument in block 27. For example, if the signal to noise ratiowas too low in a first data set, the instrument may be adjusted to testthe same sample, but at a different spot on the sample. By moving to anew target spot, new data may be acquired for the same sample. Intesting the new spot, it is quite possible that different or betteranalytical results may be found. Thus, taking a reading at a second spotmay enable making an analytical call on a sample when it was notpossible with only a single spot test. Further, testing additional spotson an individual sample may permit the calculation of aggregate resultswith a lower error rate than relying solely on a single test spot. Byautomating the evaluation of the acquired data and control of theinstrument, the overall assay test can be manipulated to provide arequisite level of accuracy and tolerance. Accordingly, the maximumnumber of samples can be accurately called for a particular assay, butyet time and system resources are not wasted by testing more spots thannecessary.

[0036] After the instrument is adjusted and set to acquire a next dataset, the method returns to block 25 to acquire the next data set. Asdescribed above, the next data set may be for the same sample, or theinstrument may have been adjusted to the next sample. After testing iscompleted, processing moves to block 28.

[0037] Block 28 shows that the results from the acquired data areanalyzed to determine the presence of an object biological relationship.For example, the assay may be attempting to locate particular singlenucleotide polymorphisms (SNPs), or may be allele typing, or may begenotyping. Irrespective of the particular biological relationshipsearched for, the relative success of the search may be used by the FIG.2 testing method in directing further data acquisitions. For example, ifin a multiple sample assay, the biological relationship is ruled outafter only the first sample, then the method can be directed to skiptesting the rest of the samples in the assay and move on. In anotherexample, if after testing multiple samples for a particular assay theresults are still ambiguous, block 28 can be used to determine if theambiguity can be removed by increasing the certainty of the results fora particular sample. If so, the test can be directed by the workstationto automatically take additional data acquisitions and attempt tosalvage the assay. Without such an automated and intelligent process,the assay would be rejected. Accordingly, the FIG. 2 testing methodprovides a higher level of calls, and a higher level of call certaintythan with known testing methods.

[0038] Referring now to FIG. 3, another method of testing a biologicalsample is shown. The FIG. 3 testing method 40 generally has a controlloop 42, an initialization loop 41, and a results loop 43. The controlloop 42 is responsible for acquiring data sets, comparing the data setsto predefined spectrum criteria, and adjusting the instrument responsiveto the evaluation of the acquired data. In this regard, the control loopmust operate efficiently enough to permit the timely operation of theoverall test system. Therefore, certain of the setup and storagefunctions have been off-loaded to the background loops 41 and 43. Itwill be appreciated that more or less functionality may be placed in thebackground loops to accommodate different response times needed in thecontrol loop 42.

[0039] The initialization loop 41 is a background loop that permitsstorage of assay design and plate information in block 44. Preferably,the assay design and plate information is stored in a database form.Preferably, the database of assay design and plate information may beused by multiple test systems, and may be accessed remotely. In such amanner a remote researcher may define an assay in a single database, andthat newly defined assay may be operated on multiple test systems.

[0040] Since extracting and converting the assay information intocontrol information is a time consuming process, the extraction processis performed in block 45. Of course, it will be appreciated that astypical computer workstation computational powers increase, it may bedesirable to have the extraction process made a part of the control loop42. Since the extracting step is preferably a background step, theextraction process may be performed for a next assay while the controlloop 42 is actively performing an assay. Thus, when the control loop hasfinished an assay, the extracted information from block 45 may be sentto block 51 to start the control loop 42 for a next assay.

[0041] The information from block 45 is received in block 51, where theinformation is used to initialize the instrument. In a preferredembodiment, the instrument is a MALDI TOF mass spectrometer. Theinitialization commands may include identifying the first sample totest, the proper power settings, and the desired filtering for the data.

[0042] A sample is selected for test in block 52, and data is acquiredfrom the test sample in block 53. The acquired data may be sufficientlyprocessed to determine target characteristics for the acquired data. Forexample, if signal to noise ratio is an important indication of testquality, then a signal to noise ratio may be calculated for the acquireddata. More particularly, the acquired data will be processed tofacilitate comparison with predefined spectrum criteria.

[0043] The predefined spectrum criteria, as previously discussed, definethe analytical characteristics for good data. In block 54, the acquireddata is compared to the predefined spectrum criteria. If the acquireddata is good, a “YES” outcome at block 54, then the acquired data isfurther processed in block 57 to extract biological information, and thedata is formatted and displayed in block 58. However, if the acquireddata is not good, a “NO” outcome at block 54, then block 55 asks if themaximum number of spots have been shot for this sample. For example, atypical mass spectrometer can take a maximum of about 15 to 20 shots onany given sample. To assure the integrity of the test, it may beadvisable to set the maximum to a safe number, such as 10. The sample isnot further processed if the maximum shots have been exceeded. Thus, ifless than 10 spots have been shot, a “NO” outcome at block 55, then theinstrument is adjusted to a new spot in block 56, and data is acquiredon the new spot in block 53. In block 54, the newly acquired data iscompared to the spectrum criteria. Alternatively, block 54 can useaggregated data from multiple test spots to determine if the aggregateddata is good.

[0044] Once a sample has been judged good or bad, then block 59 asks ifthere are more samples in the assay. If so, a “YES” outcome at block 59,then the instrument is adjusted in block 61 to shoot the next sample. Ifall the samples have been tested, a “NO” outcome at block 59, then thecontrol loop 42 resets and a next assay is initiated.

[0045] When the control loop 42 is complete, then the results from theassay are passed to the background results loop 43. The results loop 43may perform additional post processing on the data in block 63, whichmay include a manual review of the results. The data and results maythen be stored in block 65. Preferably, the data and results are storedin a database that is accessible from remote locations so a remoteresearcher or other test operators may review the results.

[0046] Referring now to FIG. 4, another testing method 70 isillustrated. The testing method 70 allows an assay designer to establisha minimum standard for each biological sample in block 71. Moreparticularly, the testing method 70 is directed to increasing theconfidence in the results from each sample. As discussed above, atypical mass spectrometer can take a data set from multiple spots on asingle biological sample. The testing method 70 enables the test todramatically increase the confidence for each sample, while minimizingthe number of testing samples that must be acquired.

[0047] In the testing method 70, a biological sample is selected inblock 72, and a data set is acquired in block 73. In block 74, theacquired data is evaluated against the data criteria set for the sample.For example, the data criteria may expect a signal to noise ratio toexceed a floor value. In this regard, each data set acquired for aparticular sample is compared against the data criteria. Alternatively,data collected from multiple shots in the same sample may be used in thecomparison. For example, the data criteria may require that the standarddeviation between spots on the same sample not exceed a particularvalue. Thus the comparison step could include determining the standarddeviation for all spots in the single sample to determine if confidenceis sufficiently high to call the sample. It will be appreciated that thecomparison step may entail a wide range of analytical and algorithmiccalculations, either on individual data sets or aggregates of data sets.

[0048] Importantly, the testing method 70 permits setting the datacriteria in a manner that minimizes the number of data acquisitions. Forexample, the data criteria could be accept a sample when a single dataset has a signal to noise ratio meeting one level, or meeting a lowerlevel for aggregate data sets. Thus, a single strong reading would besufficiently robust, and multiple shots would not be needed on thatsample. In a similar manner, the comparison could be set to acceptsample data if the standard deviation between two successive shots isless than 5%, or accept the data if the standard deviation is less than7% for 3 shots, or less than 10% for 4 or more shots. Such flexible datacriteria permit the assay designer to set a high degree of confidencewith a minimum of data readings. Accordingly, the test system 70operates at high degree of efficiency and accuracy as compared to knownsystems.

[0049] Once the data criteria have been met, a “YES” outcome at block75, the results are stored in block 76, preferably in a database, andthe instrument adjusted to move to the next sample in block 77.Accordingly, a new sample is selected in block 72.

[0050] If the data criteria have not yet been met, a “NO” outcome atblock 75, then block 78 asks if there are any remaining spots on thesample. If unshot spots exist, a “NO” outcome at block 78, theinstrument is adjusted in block 79 to acquire data from a new spot, andthe data is acquired in block 73. If the data criteria are not met, andthere are no unshot spots, a “YES” outcome at block 78, then thatparticular sample is rejected, and the test moves on to a new sample.

[0051] Referring now to FIG. 5, a diagnostic testing method 100 isdisclosed. The diagnostic testing method is directed to finding arelationship among a set of samples that proves a particular biologicalrelationship exists. For example, certain clinical diagnostics may lookat multiple samples from an individual before identifying that theindividual is at risk for a particular disease. The diagnostic testingmethod enables such a clinical diagnosis at a level of certainty and alevel of efficiency not available in known systems.

[0052] The diagnostic testing method 100 receives an assay design andrelationship criteria at block 101. The relationship criteria define therange of values and certainties where a relationship can be identified.In a preferred embodiment, the relationship is the likelihood that aparticular individual will contact a particular disease. Due to theseriousness of the identification, it is crucial that such anidentification be made only under the most confident conditions.Accordingly, known systems have required redundancies and over-testingto build confidence sufficient to make such a drastic announcementregarding an individual's health.

[0053] In block 102, a set of samples is identified for testing for therelationship. As there are likely several, even tens of samples to test,it is also likely that the set of samples may be present on multipleholders. Thus the testing method 100 should account for instructing anoperator or a robot to deliver and load different holders as needed.

[0054] A particular sample is selected from the set in block 103, anddata acquired from the sample in block 104. The acquired data isevaluated against the relationship criteria in block 105. In a preferredembodiment, testing system 100 incorporates aspects of previouslydiscussed testing system 70 to increase the confidence that the resultsfrom an individual sample are robust. The previously discussed method ofover-sampling a single biological sample can dramatically increase theconfidence in the data from a single sample.

[0055] In block 106, the acquired data is evaluated to determine if itsupports the object relationship. If the data does not support theobject relationship, a “NO” outcome, then it is reported that therelationship does not exist in the set in block 111, and the test moveson to the next set of samples in block 110. Due to the high degree ofconfidence in sample results, it is possible for the testing method 100to reject the entire sample and move to the next set. Accordingly, thetesting method 100 may operate efficiently.

[0056] If block 106 finds that the data does support the relationship, a“YES” outcome, then block 107 asks if the data acquired thus farconclusively proves the relationship exists. If enough data has beencollected, and the relationship proved, a “YES” outcome at block 107,then block 112 reports that the relationship exists, and the test moveson to the next set of samples. Thus, the testing method 100 only takesthe necessary number of data acquisitions to call a diagnosis, enablingefficient operation.

[0057] If block 107 finds that the collected data does not prove thebiological relationship, a “NO” outcome, then block 108 asks if thereare any more samples to be tested in the sample set. If no more samplesexist, a “NO” outcome at block 108, then block 113 reports that therelationship could not be proved, and the test moves on to the nextsample set. If there are more samples to be tested, then the instrumentis adjusted to the next sample in block 109, and data acquired from thenew sample in block 104.

[0058]FIG. 6 shows an example user display 130 for a test system. Theuser display 130 is preferably presented on a computer monitor connectedto an IBM compatible computer system. In a preferred embodiment, theuser display 130 is presented using a Microsoft® Windows® compatibledisplay program.

[0059] The user display 130 has a spectrum window 132 for displaying adata spectrum of the most recently acquired data set. The spectrumwindow 132 enables an operator to watch, in near real-time, the databeing collected by the instrument. If multiple spots are shot for aparticular sample, each successive data spectrum may be displayed in adifferent color so variations between spots is easily identified.

[0060] The user display also has a holder representation 134. The holderrepresentation of FIG. 6 shows individual sample wells in a microtiterplate. For example, a well representation shows the wells in a physicalmicrotiter plate holder. As each well is tested, the well representationturns a different color base on whether the sample was accepted orrejected. A results display 138 shows assay data and a results qualitydisplay 140 shows run data for data sets. Accordingly, as the testprogresses, an operator may identify certain systemic problems. Forexample, if all wells in a particular column fail, then there may be aproblem with the syringe used to fill that particular column.

[0061] The user interface 130 also has a sample view 136 which shows alive image of the sample currently being tested. With this view, anoperator may visually identify spots that have been used within aparticular sample. Also, the operator may be able to identify certainsystemic problems, such as a too small sample being deposited intocertain wells.

What is claimed is:
 1. A system for performing a biological assay,comprising: an instrument configured to acquire biological data from abiological sample; and a processor that communicates with theinstrument, such that the processor directs the instrument to acquiredata indicative of the biological sample, establishes a data spectrumcriteria, generates data parameters using the acquired data, comparesthe data parameters to the spectrum criteria, adjusts the instrumentresponsive to evaluating the data, and directs the instrument to acquireother data for the biological assay.
 2. The system according to claim 1wherein the processor further performs the step of receiving an assaydesign.
 3. The system according to claim 1 further including a databasein communication with the processor, wherein the database holds assayinformation.
 4. The system according to claim 3 wherein the processorfurther performs the steps of: receiving a portion of the assayinformation from the database; and using the received portion of theassay information to adjust the instrument.
 5. The system according toclaim 1 where the instrument is configured as a mass spectrometer. 6.The system according to claim 1 where the processor is configured as acomputer device coupled to the instrument.
 7. The system according toclaim 1 where the processor is configured as a computer device in theinstrument.
 8. The system according to claim 1 wherein the step ofgenerating the data parameters includes generating a data parameterindicative of standard deviation.
 9. The system according to claim 1wherein the processor generates the data parameters by generating a dataparameter indicative of statistical probability.
 10. The systemaccording to claim 1 wherein the processor generates the data parametersby generating a data parameter indicative of allele probability.
 11. Asystem for testing a biological sample, comprising: an instrumentconfigured to acquire biological data from the biological sample; aprocessor communicating to the instrument, the processor performingsteps comprising: directing the instrument to acquire data indicative ofthe biological sample; evaluating the acquired data; adjustingautomatically the instrument responsive to evaluating the data; anddirecting the instrument to acquire other data indicative of thebiological sample.
 12. The system for testing a biological sampleaccording to claim 11 wherein the processor further performs the stepscomprising: establishing a spectral criteria; and evaluating theacquired data using the spectral criteria.
 13. A system for performing adiagnostic assay using a set of biological samples, comprising: aninstrument configured to acquire biological data from the biologicalsamples; a processor communicating to the instrument, the processorperforming the steps comprising: directing the instrument to acquiredata indicative of one of the biological samples in the set; evaluatingthe acquired data; determining if the acquired data supports adiagnostic conclusion; and directing the instrument to acquire dataindicative of a next one of the biological samples in the set responsiveto the determining step.
 14. A system for performing a diagnostic assayusing a set of biological samples, the system comprising: a workstationthat communicates with an instrument that is configured to acquirebiological data from successive biological samples in the set, and thatcontrols the instrument to acquire data indicative of each successivebiological sample, determines if the instrument should be adjusted inresponse to evaluating the acquired data from a set, and directs theinstrument to acquire other data indicative of the biological sampleresponsive to the determination; and a database server that stores theacquired data from the biological samples.
 15. The system according toclaim 14, wherein the workstation evaluates the acquired data,determines if the acquired data supports a diagnostic conclusion, anddirects the instrument to acquire data indicative of a next one of thebiological samples in the set, responsive to the determination.
 16. Thesystem according to claim 14, wherein the workstation includes an assaydesign controller that acquires assay design specifications from thedatabase server.
 17. The system according to claim 14, wherein theworkstation includes an alignment controller that automatically aligns alaser of the instrument on one of the biological samples and controlsmovement of the sample in the instrument so as to receive biologicaldata from the instrument.
 18. The system according to claim 17, whereinthe workstation includes a data controller that receives a data signalfrom the alignment controller and makes the determination of directingthe instrument to acquire other data indicative of the biologicalsample, in response to the determination.
 19. The system according toclaim 14, wherein the workstation is constructed integrally with theinstrument.
 20. A method of performing a diagnostic assay using a set ofbiological samples, the method comprising: directing an instrument toacquire data indicative of one of the biological samples in the set;evaluating the acquired data; determining if the acquired data supportsa diagnostic conclusion; and directing the instrument to acquire dataindicative of a next one of the biological samples in the set responsiveto the determination.
 21. The method according to claim 20, furthercomprising: establishing a data spectrum criteria; generating dataparameters using the acquired data; comparing the data parameters to thespectrum criteria, and adjusting the instrument responsive to evaluatingthe data.
 22. The method according to claim 21, wherein generating thedata parameters includes generating a data parameter indicative ofstandard deviation.
 23. The method according to claim 21, whereingenerating the data parameters includes generating a data parameterindicative of statistical probability.
 24. The method according to claim21, wherein generating the data parameters includes generating a dataparameter indicative of allele probability.
 25. The method according toclaim 21, further including receiving an assay design.
 26. The methodaccording to claim 21, further including storing the acquired data fromthe biological samples of the set in a database server.
 27. The methodaccording to claim 26, further including: receiving a portion of theassay information from the database server; and using the receivedportion of the assay information to adjust the instrument.